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on Marketing |
| By: | Lankheit, Sophie; Hempel, Corinna |
| Abstract: | Through the application of a Q methodological approach, this study captures consumers’ viewpoints on grocery shopping, food preparation, and food consumption during the COVID-19 pandemic. Lockdowns of public life that were implemented along with social distancing guidelines shifted consumption from outside to inside consumers’ homes and interrupted consumers’ routines. Various changes in consumer behavior occurred, such as hoarding, buying more local food, and preparing meals at home. Since there is still a lack of socio-economic research on factors influencing changes in consumer behavior during the COVID-19 pandemic, this study provides a basis for further research by analyzing consumers’ beliefs in a holistic manner. Four viewpoints on the changes in consumer behavior during the pandemic are identified, varying in their focus on food preparation, grocery shopping, the risk of infection, or conscious consumption. The findings suggest that the pandemic has changed the mindset of only some consumers towards more sustainable consumption practices, although it is often considered as a catalyst for sustainable behavior. To encourage further development in this respect, policies and marketing activities should be aimed at beliefs already held by consumers. Messages should thus be designed with the identified viewpoints in mind. |
| Keywords: | Food Consumption / Nutrition / Food Safety, Marketing, Research Methods / Statistical Methods |
| Date: | 2021–11–18 |
| URL: | http://d.repec.org/n?u=RePEc:ags:gewi21:317055&r= |
| By: | Stefan Vamosi; Michael Platzer; Thomas Reutterer |
| Abstract: | AI-based face recognition, i.e., the re-identification of individuals within images, is an already well established technology for video surveillance, for user authentication, for tagging photos of friends, etc. This paper demonstrates that similar techniques can be applied to successfully re-identify individuals purely based on their behavioral patterns. In contrast to de-anonymization attacks based on record linkage, these methods do not require any overlap in data points between a released dataset and an identified auxiliary dataset. The mere resemblance of behavioral patterns between records is sufficient to correctly attribute behavioral data to identified individuals. Further, we can demonstrate that data perturbation does not provide protection, unless a significant share of data utility is being destroyed. These findings call for sincere cautions when sharing actual behavioral data with third parties, as modern-day privacy regulations, like the GDPR, define their scope based on the ability to re-identify. This has also strong implications for the Marketing domain, when dealing with potentially re-identify-able data sources like shopping behavior, clickstream data or cockies. We also demonstrate how synthetic data can offer a viable alternative, that is shown to be resilient against our introduced AI-based re-identification attacks. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.10351&r= |